Deep learning-based prediction of response to neoadjuvant immunochemotherapy in triple-negative breast cancer based on pretreatment DCE-MRI

Le résumé fourni par la source

Motivation: Accurate prediction of the response to neoadjuvant immunochemotherapy (NICT) in triple negative breast cancer (TNBC) is valuable in guiding individualized treatment. Goal(s): To develop a deep learning-based model to predict response before the initiation of NICT. Approach: Our deep-learning model used pretreatment DCE-MRI from 100 TNBC patients. Results: Although preliminary and limited by a small patient number, our model yielded an average AUC of 0.63 in predicting the response to NICT in an independent testing. Impact: A deep learning model has the potential to predict TNBC response to NICT before treatment and to help with the clinical management.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.